Internet traffic classification via time-frequency analysis

ABSTRACT

Concepts and technologies disclosed herein are directed to internet traffic classification via time-frequency analysis. According to one aspect of the concepts and technologies disclosed herein, a security classification scheme can be implemented to identify potentially malicious activities from normal internet traffic. The security classification scheme can exploit the distinctive characteristics of different types of traffic in both frequency domain and time domain to identify four different cases. Due to the separation of different types of traffic, the security classification scheme can lower the false alarm rate and improve network security. The security classification scheme can utilize a recursive discrete Fourier transform (“DFT”) implementation to enhance computational efficiency. The security classification scheme can be deployed for real-time network traffic monitoring due to an efficient streaming design and can be effectively used to detect and predict when and where the suspicious activities occur within a monitored network.

BACKGROUND

Malicious network traffic can cause disruptions in network services,consumer fraud, loss of information, and other problems. Malicioustraffic is typically hidden within normal traffic, the majority of whichis consumer traffic (“CT”) and machine-to-machine (“M2M”) traffic. Sincean attacker will attempt to hide or obfuscate malicious traffic, it isdifficult for network providers to detect and to predict when and wheremalicious traffic will appear. Techniques for detecting maliciousactivity also are prone to false alarms when normal traffic cannot beseparated from traffic associated with malicious activities.

Most existing malicious activity detection techniques focus on analyzingtime-domain characteristics. Some techniques apply correlations betweenunknown traffic and known traffic patterns. Other techniques set upalarm thresholds for pre-warning or following examinations. Yet othertechniques use a training set for supervised learning. Moreover,existing techniques cannot be implemented with streaming network trafficdue to computational complexities.

SUMMARY

Concepts and technologies disclosed herein are directed to internettraffic classification via time-frequency analysis. According to oneaspect of the concepts and technologies disclosed herein, an internettraffic classification system can receive an internet traffic sequencethat includes non-malicious data packets and malicious data packets. Theinternet traffic classification system can extract, from the internettraffic sequence, a plurality of consecutive samples to be used forclassification of the internet traffic sequence. The internet trafficclassification system can convert the plurality of consecutive samplesof the internet traffic sequence from a time domain to a frequencydomain via a discrete Fourier transform. The internet trafficclassification system can determine whether a largest power spectrum inthe plurality of consecutive samples of the internet traffic sequence isgreater than a threshold portion of a total power spectra of theplurality of consecutive samples of the internet traffic sequence. Whenthe largest power spectrum in the plurality of consecutive samples ofthe internet traffic sequence is greater than the threshold portion ofthe total power spectra, the internet traffic classification system candetermine that the plurality of consecutive samples of the internettraffic sequence includes a consumer traffic component and can remove,from the plurality of consecutive samples of the internet trafficsequence, any samples of the plurality of consecutive samplescorresponding to the consumer traffic component. The internet trafficclassification system can calculate a mean and a variance of a remainingportion of the internet traffic sequence. The remaining portion of theinternet traffic sequence can include the plurality of consecutivesamples without any samples corresponding to the consumer trafficcomponent. The internet traffic classification system can set, basedupon the mean and the variance of the remaining portion of the internettraffic sequence, a threshold for detection of M2M traffic. The internettraffic classification system can record a series of time indices forsamples in the remaining portion of the internet traffic sequence thatare greater than the threshold for detection of M2M traffic. Theinternet traffic classification system can compute time differencesbetween adjacent time indices within the series of time indices. Theinternet traffic classification system can create a histogram using thetime differences. The internet traffic classification system can countthe histogram. When most occurrences in the histogram are in associationwith a specific time difference, the internet classification system candetermine that the remaining portion of the internet traffic sequenceincludes an M2M traffic component.

In some embodiments, the internet classification system can classify theinternet traffic sequence as including the consumer traffic componentonly. In other embodiments, the internet classification system canclassify the internet traffic sequence as including the M2M trafficonly. In other embodiments, the internet classification system canclassify the internet traffic sequence as including the computer trafficcomponent and the M2M traffic component. In other embodiments, theinternet classification system can classify the internet trafficsequence as including an unknown traffic component.

In some embodiments, the internet classification system can perform theaforementioned operations through a sliding window. The sliding windowcan focus on one sample of the plurality of consecutive samples.

In some embodiments, the internet classification system can convert theplurality of consecutive samples of the internet traffic sequence from atime domain to a frequency domain via a recursive discrete Fouriertransform. In these embodiments, the computational complexity of theconversion can be one order magnitude lower.

It should be appreciated that the above-described subject matter may beimplemented as a computer-controlled apparatus, a computer process, acomputing system, or as an article of manufacture such as acomputer-readable storage medium. These and various other features willbe apparent from a reading of the following Detailed Description and areview of the associated drawings.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intendedthat this Summary be used to limit the scope of the claimed subjectmatter. Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an illustrative operatingenvironment capable of implementing aspects of the concepts andtechnologies disclosed herein.

FIGS. 2A-2B are flow diagrams illustrating aspects of a method forclassifying internet traffic, according to an illustrative embodiment.

FIGS. 3A-3C are flow diagrams illustrating aspects of a more detailedmethod for classifying internet traffic, according to an illustrativeembodiment.

FIG. 4 is a diagram illustrating aspects of consumer traffic and M2Mtraffic before and after applying a discrete Fourier transform (“DFT”),according to an illustrative example.

FIG. 5 is a block diagram illustrating an example computer system andcomponents thereof capable of implementing aspects of the embodimentspresented herein.

FIG. 6 is a block diagram illustrating an example mobile device andcomponents thereof capable of implementing aspects of the embodimentspresented herein.

FIG. 7 is a block diagram illustrating an example network capable ofimplementing aspects of the embodiments presented herein.

FIG. 8 is a block diagram illustrating an example network functionvirtualization platform capable of implementing aspects of theembodiments presented herein.

DETAILED DESCRIPTION

The concepts and technologies disclosed herein are directed to internettraffic classification via time-frequency analysis. According to oneaspect of the concepts and technologies disclosed herein, a securityclassification scheme can be implemented to identify potentiallymalicious activities from normal internet traffic. The securityclassification scheme can exploit the distinctive characteristics ofdifferent types of traffic in both frequency domain and time domain toidentify four different cases. Due to the separation of different typesof traffic, the security classification scheme can lower the false alarmrate and improve network security. The security classification schemecan utilize a recursive discrete Fourier transform (“DFT”)implementation to enhance computational efficiency. The securityclassification scheme can be deployed for real-time network trafficmonitoring due to an efficient streaming design and can be effectivelyused to detect and predict when and where the suspicious activitiesoccur within a monitored network.

The recursive approach utilized by the disclosed security classificationscheme can classify potentially malicious traffic from consumer traffic,machine-to-machine (“M2M”) traffic, or combination consumer traffic andM2M traffic. Since consumer traffic and M2M traffic have distinctivefrequency spectrum profiles, the disclosed security classificationscheme can convert an internet traffic sequence to frequency spectra viaDFT. For example, consumer traffic might have a frequency spectrumprofile that exhibits a single spike while M2M traffic might have afrequency spectrum profile with outspread and identically repeatingspikes.

The disclosed security classification scheme can detect consumer trafficin terms of a ratio of the largest power spectrum over the total powerspectra. If the ratio is higher than a threshold, consumer trafficlikely exists in the traffic sequence. A time-domain formula can be usedto subtract the consumer traffic from the traffic sequence, in whichcase no inverse DFT is needed. If the ratio is lower than the threshold,no consumer traffic is detected, and therefore it can be determined thatthe traffic sequence includes only M2M traffic (i.e., no subtraction isneeded). After subtraction the disclosed security classification schemecan calculate a mean and a variance of the remaining traffic sequence toset a threshold for detection of M2M traffic in the remaining trafficsequence. If a given sample of the remaining traffic sequence is greaterthan the threshold, the time index for that sample is recorded;otherwise, the sample is skipped. After obtaining a series of timeindices for a plurality of samples, a histogram can be generated, thetime differences between adjacent time indices can be computed, and thehistogram can be counted. When the most occurrences happen on a specifictime interval in the histogram, this can be determined to be indicativethat M2M traffic is contained in the remaining traffic sequence. Basedupon the two detections with consumer traffic and M2M traffic, thesecurity classification scheme can classify internet traffic into one offour traffic profiles: consumer traffic, consumer traffic and M2Mtraffic, M2M traffic, and unknown. The aforementioned operations can beimplemented through a sliding window with one sample shift each time.Since most computations are from DFT, the security classification schemeuses recursive DFT to decrease the computational complexity of thesecomputations by at least one order of magnitude. As a recursiveclassifier, the disclosed classification scheme can be deployed forreal-time network traffic monitoring for pre-warning or anomalydetection.

While the subject matter described herein may be presented, at times, inthe general context of program modules that execute in conjunction withthe execution of an operating system and application programs on acomputer system, those skilled in the art will recognize that otherimplementations may be performed in combination with other types ofprogram modules. Generally, program modules include routines, programs,components, data structures, computer-executable instructions, and/orother types of structures that perform particular tasks or implementparticular abstract data types. Moreover, those skilled in the art willappreciate that the subject matter described herein may be practicedwith other computer systems, including hand-held devices, mobiledevices, wireless devices, multiprocessor systems, distributed computingsystems, microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers, routers, switches, other computingdevices described herein, and the like.

In the following detailed description, references are made to theaccompanying drawings that form a part hereof, and in which are shown byway of illustration specific embodiments or examples. Referring now tothe drawings, in which like numerals represent like elements throughoutthe several figures, aspects of concepts and technologies internettraffic classification via time-frequency analysis will be described.

Turning now to FIG. 1, an operating environment 100 in which aspects ofthe concepts and technologies disclosed herein can be implemented willbe described, according to an embodiment. The illustrated operatingenvironment 100 includes a plurality of user equipment devices (“UEs”)102A-102N (referred to herein collectively as UEs 102, or generally inthe singular form as UE 102) operating in communication with a radioaccess network (“RAN”) 104. The UEs 102 can communicate with the RAN 104by way of one or more eNodeBs (“eNBs”) 106. The operating environment100 also includes a plurality of M2M devices 108A-108N (referred toherein collectively as M2M devices 108, or generally in the singularform as M2M device 108) also operating in communication with the RAN 104by way of one or more eNBs 106.

Each of the UEs 102 can be a cellular phone, a feature phone, asmartphone, a mobile computing device, a tablet computing device, aportable television, a portable video game console, or the like capableof communicating with the RAN 104. The RAN 104 can include one or moreservice areas (which may also be referred to herein as “cells”) havingthe same or different cell sizes, which may be represented by differentcell-types. As used herein, a “cell” refers to a geographical area thatis served by one or more base stations operating within an accessnetwork. The cells within the RAN 104 can include the same or differentcell sizes, which may be represented by different cell-types. Acell-type can be associated with certain dimensional characteristicsthat define the effective radio range of a cell. Cell-types can include,but are not limited to, a macro cell-type, a metro cell-type, a femtocell-type, a pico cell-type, a micro cell-type, wireless local areanetwork (“WLAN”) cell-type, a multi-standard metro cell (“MSMC”)cell-type, and a white space network cell-type. Other cell-types,including proprietary cell-types and temporary cell-types are alsocontemplated. Although in the illustrated example, the UEs 102 are shownas being in communication with one RAN (i.e., the RAN 104), the UEs 102may be in communication with any number of access networks, includingnetworks that incorporate collocated WWAN WI-FI and cellulartechnologies, and as such, one or more of the UEs 102 can be a dual-modedevice.

The M2M devices 108, in some embodiments, form, at least in part, an IoTnetwork (not shown). The M2M devices 108 can be deployed across variousindustry segments and embedded in a variety of locations, such asbasements in multi-dwelling units, underground tunnels, manholes, subwaysystems, and/or the like, where there could be emergency situations thatneed to be handled to protect safety of humans, machines, and theirinteractions. IoT is a concept of making physical objects, collectively“things,” also referred to herein as the M2M devices 108, networkaddressable to facilitate interconnectivity for the exchange of data.The IoT network can include any number of “things,” including the M2Mdevices 108, for example. The M2M devices 108 can be or can include any“thing” that can collect data and that is configured to be networkaddressable so as to connect to and communicate with one or morenetworks, such as the RAN 104, over which to communicate the data toother connected devices, including, for example, computers, smartphones,tablets, vehicles, other M2M devices, combinations thereof, and thelike. The M2M devices 108 can be deployed for consumer use and/orbusiness use, and can find application in many industry-specific usecases. For example, the M2M devices 108 may find at least partialapplication in the following industries: automotive, energy, healthcare,industrial, retail, and smart buildings/homes. Those skilled in the artwill appreciate the applicability of M2M-solutions in other industriesas well as consumer and business use cases. For this reason, theapplications of the M2M devices 108 described herein are used merely toillustrate some examples and therefore should not be construed as beinglimiting in any way. Although in the illustrated example the M2M devices108 are shown as being in communication with one RAN (i.e., the RAN104), the M2M devices 108 may be in communication with any number ofaccess networks, including networks that incorporate collocated WWANWI-FI and cellular technologies, and as such, one or more of the M2Mdevices 108 can be a dual-mode device.

The RAN 104 can operate in accordance with one or more mobiletelecommunications standards including, but not limited to, GlobalSystem for Mobile communications (“GSM”), Code Division Multiple Access(“CDMA”) ONE, CDMA2000, Universal Mobile Telecommunications System(“UMTS”), LTE, Worldwide Interoperability for Microwave Access(“WiMAX”), other current 3GPP cellular technologies, other future 3GPPcellular technologies, combinations thereof, and/or the like. The RAN104 can utilize various channel access methods (which may or may not beused by the aforementioned standards), including, but not limited to,Time Division Multiple Access (“TDMA”), Frequency Division MultipleAccess (“FDMA”), CDMA, wideband CDMA (“W-CDMA”), Orthogonal FrequencyDivision Multiplexing (“OFDM”), Single-Carrier FDMA (“SC-FDMA”), SpaceDivision Multiple Access (“SDMA”), and the like to provide a radio/airinterface to the UEs 102 and the M2M devices 108. Data communicationscan be provided in part by the RAN 104 using General Packet RadioService (“GPRS”), Enhanced Data rates for Global Evolution (“EDGE”), theHigh-Speed Packet Access (“HSPA”) protocol family including High-SpeedDownlink Packet Access (“HSDPA”), Enhanced Uplink (“EUL”) or otherwisetermed High-Speed Uplink Packet Access (“HSUPA”), Evolved HSPA(“HSPA+”), LTE, and/or various other current and future wireless dataaccess technologies. Moreover, the RAN 104 may be a GSM RAN (“GRAN”), aGSM EDGE RAN (“GERAN”), a UMTS Terrestrial Radio Access Network(“UTRAN”), an evolved UTRAN (“E-UTRAN”), any combination thereof, and/orthe like. The concepts and technologies disclosed herein will bedescribed in context of the RAN 104 operating in accordance with LTE,although those skilled in the art will appreciate the applicability ofthe concepts and technologies disclosed herein to other cellulartechnologies, including, in particular, those captured within futuregeneration 3GPP standards. Moreover, in some embodiments, the RAN 104 isor includes one or more virtual RANs (“vRANs”).

As used herein, a “base station” refers to a radio receiver and/ortransmitter (collectively, transceiver) that is/are configured toprovide a radio/air interface over which the UEs 102 and the M2M devices108, can connect to a network 110. Accordingly, a base station isintended to encompass one or more base transceiver stations (“BTSs”),one or more NBs, one or more eNBs (e.g., the eNB 106), one or more homeeNBs (not shown), one or more wireless access points (“APs”), one ormore MSMC nodes, and/or other networking nodes or combinations thereofthat are capable of providing a radio/air interface regardless of thetechnologies utilized to do so. A base station can be in communicationwith one or more antennas (not shown), each of which may be configuredin accordance with any antenna design specifications to provide aphysical interface for receiving and transmitting radio waves to andfrom one or more devices, such as the UEs 102 and the M2M devices 108.

The network 110 can include one or more core networks, such as one ormore evolved packet core (“EPC”) networks. The network 110 embodied inthis manner can provide EPC network functions for the UEs 102 and theM2M devices 108. In some embodiments, the network 110 includes an EPCnetwork for the UEs and another EPC network for the M2M devices 108. Asan EPC network, the network 110 can include one or more mobilitymanagement entities (“MME”), one or more application servers (“AS”), oneor more home subscriber servers (“HSS”), one or more evolved servingmobile location centers (“ESMLC”), one or more gateway mobile locationcenters (“GMLC”), one or more serving gateways (“SGW”), one or morepacket data network gateways (“PGWs”), some combination thereof, and/orthe like. These network functions can be implemented as physical networkfunctions (“PNFs”) having hardware and software components. The corenetwork components can additionally or alternatively be provided, atleast in part, by virtual network functions (“VNFs”). For example, thecore network components can be realized as VNFs that utilize a unifiedcommercial-off-the-shelf (“COTS”) hardware and flexible resources sharedmodel with the application software for the respective core networkcomponents running on one or more virtual machines (“VMs”). An examplenetwork virtualization platform (“NVP”) architecture that might be usedto implement various core network components embodied as VNFs isdescribed herein below with reference to FIG. 8. Moreover, the corenetwork components can be embodied as VNFs in one or more VNF pools,each of which can include a plurality of VNFs providing a particularcore network function.

The illustrated network 110 is in communication with an internet 112.The internet 112 can facilitate communications among connected computersand/or devices, such as the UEs 102 and the M2M devices 108. Theinternet 112, in some embodiments, is or includes the Internet, which iswell-known in the art and therefore not described further herein.

The operating environment 100 also includes an internet trafficclassification system 114 that can execute, via one or more processors(best shown in FIG. 5), a traffic classification module 116, a malicioustraffic alert module 118, and a mitigation module 120. Each of themodules 116-120 can include instructions that, when executed by one ormore processors, cause the internet traffic classification system 114 toperform operations described herein, such as the operations describedherein below with reference to FIGS. 2A-2B and FIGS. 3A-3C. A briefdescription of these modules will now be provided.

The traffic classification module 116 can implement a securityclassification scheme to identify potentially malicious activities fromnormal internet traffic. In the illustrated example, traffic originatingfrom one or more of the UEs 102 is illustrated as a consumer trafficcomponent 122 of internet traffic, and traffic originating from one ormore of the M2M devices 108 is illustrated as an M2M traffic component124. The consumer traffic component 122 and the M2M traffic component124 can hide malicious traffic 126, 126′.

The traffic classification module 116 can utilize the securityclassification scheme to exploit the distinctive characteristics ofdifferent types of internet traffic (e.g., consumer traffic and M2Mtraffic) in both frequency domain and time domain to identify fourdifferent cases. Due to the separation of different types of traffic,the security classification scheme can lower the false alarm rate andimprove network security. The security classification scheme can utilizea recursive DFT implementation to enhance computational efficiency. Thesecurity classification scheme can be deployed for real-time networktraffic monitoring due to an efficient streaming design and can beeffectively used to detect and predict when and where the suspiciousactivities occur within a monitored network.

The recursive approach utilized by the disclosed security classificationscheme can classify the malicious traffic 126, 126′ from the consumertraffic component 122 and/or the M2M traffic component 124. Sinceconsumer traffic and M2M traffic have distinctive frequency spectrumprofiles, the disclosed security classification scheme can convert aninternet traffic sequence to frequency spectra via DFT. For example,consumer traffic might have a frequency spectrum profile that exhibits asingle spike while M2M traffic might have a frequency spectrum profilewith outspread and identically repeating spikes.

The disclosed security classification scheme can detect consumer trafficin terms of a ratio of the largest power spectrum over the total powerspectra. If the ratio is higher than a threshold, consumer trafficlikely exists in the traffic sequence. A time-domain formula can be usedto subtract the consumer traffic from the traffic sequence, in whichcase no inverse DFT is needed. If the ratio is lower than the threshold,no consumer traffic is detected, and therefore it can be determined thatthe traffic sequence includes only M2M traffic (i.e., no subtraction isneeded). After subtraction the disclosed security classification schemecan calculate a mean and a variance of the remaining traffic sequence toset a threshold for detection of M2M traffic in the remaining trafficsequence. If a given sample of the remaining traffic sequence is greaterthan the threshold, the time index for that sample is recorded;otherwise, the sample is skipped. After obtaining a series of timeindices for a plurality of samples, a histogram can be generated, thetime differences between adjacent time indices can be computed, and thehistogram can be counted. When the most occurrences happen on specifictime interval in the histogram, this can be determined to be indicativethat M2M traffic is contained in the remaining traffic sequence. Basedupon the two detection with consumer traffic and M2M traffic, thesecurity classification scheme can classify internet traffic into one offour traffic profiles: consumer traffic, consumer traffic and M2Mtraffic, M2M traffic, and unknown. The aforementioned operations can beimplemented through a sliding window with one sample shift each time.Since most computations are from DFT, the security classification schemeuses recursive DFT to decrease the computational complexity of thesecomputations by at least one order of magnitude. As a recursiveclassifier, the disclosed classification scheme can be deployed forreal-time network traffic monitoring for pre-warning or anomalydetection. Additional details in this regard are described herein belowwith reference to FIGS. 2A-2B and FIGS. 3A-3C.

The malicious traffic alert module 118 can generate one or more alertsresponsive to detection of traffic that does not classify within one ofthe aforementioned traffic profiles. The alert(s) can be used to notifyone or more individuals and/or other entities that further investigationis needed. Anomaly detection can be launched to determine to which kindof malicious behavior the traffic belongs.

Once a specific malicious behavior pattern has been detected, thetraffic can be decomposed into lower-level detailed subcomponents. Thelower-level decomposition can reduce the complexity of traffic signals,and in turn, can facilitate the following root-cause analysis withineach individual subcomponent. After determining the root cause, themitigation module 120 can be used to mitigate the severity of risks,such as, for example, closing one or more network ports re-routing atleast some of the traffic, and/or blocking one or more types of networkconnections. Those skilled in the art will appreciate the applicabilityof other mitigation techniques to be implemented by the mitigationmodule 120.

Turning now to FIGS. 2A-2B, aspects of a method 200 for classifyinginternet traffic will be described, according to an illustrativeembodiment. It should be understood that the operations of the methodsdisclosed herein are not necessarily presented in any particular orderand that performance of some or all of the operations in an alternativeorder(s) is possible and is contemplated. The operations have beenpresented in the demonstrated order for ease of description andillustration. Operations may be added, omitted, and/or performedsimultaneously, without departing from the scope of the concepts andtechnologies disclosed herein.

It also should be understood that the methods disclosed herein can beended at any time and need not be performed in its entirety. Some or alloperations of the methods, and/or substantially equivalent operations,can be performed by execution of computer-readable instructions includedon a computer storage media, as defined herein. The term“computer-readable instructions,” and variants thereof, as used herein,is used expansively to include routines, applications, applicationmodules, program modules, programs, components, data structures,algorithms, and the like. Computer-readable instructions can beimplemented on various system configurations including single-processoror multiprocessor systems, minicomputers, mainframe computers, personalcomputers, hand-held computing devices, microprocessor-based,programmable consumer electronics, combinations thereof, and the like.

Thus, it should be appreciated that the logical operations describedherein are implemented (1) as a sequence of computer implemented acts orprogram modules running on a computing system and/or (2) asinterconnected machine logic circuits or circuit modules within thecomputing system. The implementation is a matter of choice dependent onthe performance and other requirements of the computing system.Accordingly, the logical operations described herein are referred tovariously as states, operations, structural devices, acts, or modules.These states, operations, structural devices, acts, and modules may beimplemented in software, in firmware, in special purpose digital logic,and any combination thereof. As used herein, the phrase “cause aprocessor to perform operations” and variants thereof is used to referto causing one or more processors disclosed herein to performoperations.

For purposes of illustrating and describing some of the concepts of thepresent disclosure, the method 200 is described as being performed, atleast in part, by one of the processors via execution of one or moresoftware modules. It should be understood that additional and/oralternative devices and/or network nodes can provide the functionalitydescribed herein via execution of one or more modules, applications,and/or other software. Thus, the illustrated embodiments areillustrative, and should not be viewed as being limiting in any way.

The method 200 will be described with reference to FIGS. 2A-2B andadditional reference to FIG. 1. The operations disclosed as part of themethod 200 provide a general overview of a method for classifyinginternet traffic. Additional details of a method for classifyinginternet traffic are provided herein below with reference to FIGS.3A-3C.

Turning now to FIG. 2A, the method 200 begins and proceeds to operation202, where the internet traffic classification system 114 receives aninternet traffic sequence, including non-malicious data packets andmalicious data packets. The internet traffic sequence can include aconsumer traffic component 122 and/or an M2M traffic component 124 asshown in the example of FIG. 1, either or both of which can containmalicious traffic 126, 126′.

From operation 202, the method 200 proceeds to operation 204, where theinternet traffic classification system 114 extracts, from the internettraffic sequence, a plurality of consecutive samples to be consideredfor classification. From operation 204, the method 200 proceeds tooperation 206, where the internet traffic classification system 114converts the plurality of consecutive samples from the time domain tothe frequency domain via DFT.

From operation 206, the method 200 proceeds to operation 208, where theinternet traffic classification system 114 determines whether a largestpower spectrum in the plurality of consecutive samples is greater than athreshold portion of a total power spectra. From operation 208, themethod 200 proceeds to operation 210, where if the internet trafficclassification system 114 determines that the largest power spectrum inthe plurality of consecutive samples is greater than the thresholdportion of the total power spectra, the method 200 proceeds to operation212, where the internet traffic classification system 114 determinesthat the plurality of consecutive samples includes a consumer trafficcomponent (e.g., the consumer traffic component 122 shown in FIG. 1).

From operation 212, the method 200 proceeds to operation 214, where theinternet traffic classification system 114 removes any samples from theplurality of the consecutive samples corresponding to the consumertraffic component 122. From operation 214, the method 200 proceeds tooperation 216, where the internet traffic classification system 114calculates a mean and a variance of a remaining portion of the internettraffic sequence—that is, one or more samples remaining in the pluralityof the consecutive samples after removal of one or more samplescorresponding to the consumer traffic component 122. From operation 216,the method 200 proceeds to operation 218, where the internet trafficclassification system 114 sets, based upon the mean and the variancecalculated during operation 216, a threshold for detection of M2Mtraffic in the internet traffic sequence.

From operation 218, the method 200 proceeds to operation 220, where theinternet traffic classification system 114 records a series of timeindices for the samples in the remaining portion of the internet trafficsequence. From operation 220, the method 200 proceeds to operation 222,where the internet traffic classification system 114 computes timedifferences between adjacent time indices within the series of timeindices.

From operation 222, the method 200 proceeds to operation 224, where theinternet traffic classification system 114 creates a histogram using thetime differences computed during operation 222. From operation 224, themethod 200 proceeds to operation 226, where the internet trafficclassification system 114 counts the histogram. From operation 226, themethod 200 proceeds to operation 228 shown in FIG. 2B, where theinternet traffic classification system 114 determines whether mostoccurrences in the histogram are associated with a specific timedifference. If so, the method 200 proceeds to operation 230, where theinternet traffic classification system 114 determines that the remainingportion of the internet traffic sequence includes an M2M trafficcomponent (e.g., the M2M traffic component 124 shown in FIG. 1). If not,the method 200 to operation 236, where the internet trafficclassification system 114 determines that the remaining portion of theinternet traffic sequence does not include an M2M component.

From operation 230 or operation 236, the method 200 proceeds tooperation 232, where the internet traffic classification system 114classifies the internet traffic sequence. From operation 232, the method200 proceeds to operation 234. The method 200 ends at operation 234.

Returning to operation 210 shown in FIG. 2A, where if the internettraffic classification system 114 determines that the largest powerspectrum in the plurality of consecutive samples is not less than thethreshold portion of the total power spectra, the method 200 proceedsdirectly to operation 216, where the internet traffic classificationsystem 114 calculates a mean and a variance of a remaining portion ofthe internet traffic sequence—that is, one or more samples remaining inthe plurality of the consecutive samples after removal of one or moresamples corresponding to the consumer traffic component 122. The method200 then proceeds as described above.

Turning now to FIGS. 3A-3C, a more detailed method 300 for classifyinginternet traffic will be described, according to an illustrativeembodiment. The method 300 begins and proceeds to operation 302, wherethe internet traffic classification system 114 denotes a traffic timeseries x_(i), i=0, 1, . . . and forms vectors from N consecutive samplesin x_(i) such that x_(i)=[x_(i), x_(i+1), . . . , x_(i+N−1)]^(T) andinitiates x₀ for i=0.

From operation 302, the method 300 proceeds to operation 304, where theinternet classification system 114 converts x_(i) to a N×1 frequencyvector y_(i) via DFT such that y_(i)=DFT(x_(i)). It should be noted thatvectors x_(i) and x_(i+1) overlap (N−1) points. This overlappingproperty can be exploit to avoid the repeating calculations from y_(i)to y_(i+1), which is explained in the following recursive DFT. Denote

$\omega = {e^{i\frac{2\;\pi}{N}}.}$DFT of x_(i) and x_(i+1) be expressed as

$y_{i} = {{F\; F\;{T\left( x_{i} \right)}} = {\begin{bmatrix}1 & 1 & 1 & \ldots & 1 \\1 & \omega & \omega^{2} & \ldots & \omega^{({N - 1})} \\1 & \omega^{2} & \omega^{4} & \ldots & \omega^{2{({N - 1})}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\1 & \omega^{({N - 1})} & \omega^{2{({N - 1})}} & \ldots & \omega^{{({N - 1})}^{2}}\end{bmatrix}\begin{bmatrix}x_{i} \\x_{i + 1} \\\vdots \\x_{i + N - 2} \\x_{i + N - 1}\end{bmatrix}}}$$y_{i + 1} = {{F\; F\;{T\left( x_{i + 1} \right)}} = {\begin{bmatrix}1 & 1 & 1 & \ldots & 1 \\1 & \omega & \omega^{2} & \ldots & \omega^{({N - 1})} \\1 & \omega^{2} & \omega^{4} & \ldots & \omega^{2{({N - 1})}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\1 & \omega^{({N - 1})} & \omega^{2{({N - 1})}} & \ldots & \omega^{{({N - 1})}^{2}}\end{bmatrix}\begin{bmatrix}x_{i + 1} \\x_{i + 2} \\\vdots \\x_{i + N - 1} \\x_{i + N}\end{bmatrix}}}$where the overlapped (N−1) elements are marked in bold.

From operation 302, the method 300 proceeds to operation 306, where, fori=0, 1, 2, . . . , the internet classification system 114 determines ifthe largest power spectrum in y_(i) is greater than a threshold of thetotal power spectrum. After obtaining the vector y_(i) via DFT, it isnoted that DFT of a real signal is Hermetian symmetry—that is,y_(i)[n]=conj(y_(i)[N−n]) for all n. Due to this symmetry, to find thelargest power spectrum within y_(i), only half of the elements fromy_(i)[1] to y_(i) [N/2] are needed (skip y_(i) [0], which is a DCcomponent).

${\max\left( {{{y_{i}\lbrack 1\rbrack}}^{2},{{y_{i}\lbrack 2\rbrack}}^{2},\ldots\mspace{14mu},{{y_{i}\left\lbrack \frac{N}{2} \right\rbrack}}^{2}} \right)} = {{{y_{i}\lbrack k\rbrack}}^{2} > {\beta{\sum\limits_{n = 1}^{\frac{N}{2}}{{y_{i}\lbrack n\rbrack}}^{2}}}}$where y_(i)[n] is the nth element in vector y_(i), and β is aconfigurable threshold of maximum power spectrum over the total powerspectra.

From operation 306, the method 300 proceeds to operation 308, where ifthe internet classification system 114 determines that the largest powerspectrum in y_(i) is less than a threshold of the total power spectrum,the method 300 proceeds to operation 310, where the internetclassification system 114 denotes a new vector z_(i)=x_(i). Once themaximum power spectrum bin y_(i)[k] is determined, then the symmetricfrequency components y_(i)[k] and y_(i)[N−k] are calculated withconsumer traffic to obtain a remaining vector z_(i). The n-th elementz_(i) [n] is calculated as

${{z_{i}\lbrack n\rbrack} = {{{x_{i}\lbrack n\rbrack} - {{y_{i}\lbrack k\rbrack}\frac{e^{{jkn}\frac{2\;\pi}{N}}}{N}} - {{y_{i}\left\lbrack {N - k} \right\rbrack}\frac{e^{{- {jkn}}\frac{2\;\pi}{N}}}{N}}} = {{{x_{i}\lbrack n\rbrack} - \left( {{{y_{i}\lbrack k\rbrack}e^{{jkn}\frac{2\;\pi}{N}}} + {{{conj}\left( {y_{i}\lbrack k\rbrack} \right)}e^{{- {jkn}}\frac{2\;\pi}{N}}}} \right)} = {{x_{i}\lbrack n\rbrack} - {{Real}\left( {2 \times {y_{i}\lbrack k\rbrack}e^{{- {jkn}}\frac{2\;\pi}{N}}} \right)}}}}},{{{where}\mspace{14mu} n} = 0},1,\ldots\mspace{14mu},{N - 1.}$

From operation 310, the method proceeds to operation 312, shown in FIG.3B, where the internet classification system 114 computes a mean and avariance of z_(i) as (m_(i), σ_(i) ²) and sets an empty vector u_(i).From operation 312, the method 300 proceeds to operation 314, where theinternet classification system 114, for n=0, 1, . . . , N−1, tests eachelement in z_(i) whether z_(i)[n]>(m_(i)+γσ_(i)), where γ is aconfigurable threshold. If the result of a test is true, the method 300proceeds to operation 316, where the internet classification system 114appends the time index n as u_(i)=[u_(i); n]. Else, the method 300returns to operation 314 for the next value of n and repeats until N−1.

From operation 316, the method 300 proceeds to operation 318, whereafter the end of N loops with operations 314, 316, the internetclassification system 114 calculates D_(i)[n]=u[n+1]−u_(i)[n] for n=0,1, . . . , (L−2), where L is the total length of u_(i).

From operation 318, the method 300 proceeds to operation 320, where theinternet classification system 114 calculates if

${\frac{\max\left( {{hist}\left( d_{i} \right)} \right)}{L - 1} > T_{d}},$where hist(d_(i)) counts the number of occurrences with distinct valuesin d_(i), and T_(d) is a configurable threshold. If true, the method 300proceeds from operation 320 to operation 322, where the internetclassification system 114 classifies the traffic type as M2M only—thatis only M2M traffic is contained in z_(i). If false, the method 300proceeds to operation 324, where the internet classification system 114classifies traffic type as unknown. After operation 322 or 324, themethod 300 proceeds to operation 326, shown in FIG. 3A, where theinternet classification system 114 sets i←i+1 and updates x_(i) tox_(i+1), where x_(i+1)=[x_(i+1), x_(i+2), . . . , x_(i+N)]^(T); and letsδ_(i)=x_(i+N)−x_(i), updates y_(i) to y_(i+1) via recursive DFT suchthat y_(i+1)[n]=w^(−n)(y_(i) [n]+δ_(i)) for n=0, 1, . . . , N−1. Fromoperation 326, the method 300 returns to the loop started at operation306 and the method 300 continues as described.

Returning to operation 308, if the internet classification system 114determines that the largest power spectrum in y_(i) is greater than thethreshold of the total power spectrum, the method 300 proceeds tooperation 328, where the internet classification system 114 determinesthat y_(i) [k] is consumer traffic contained in x_(i). From operation328, the method 300 proceeds to operation 330, where the internetclassification system 114 subtracts the consumer traffic componentsy_(i) [k] and y_(i) [N−k] from x_(i). From operation 330, the method 300proceeds to operation 332, shown in FIG. 3C, where the internetclassification system 114 computes a mean and a variance of z_(i):(m_(i), σ_(i) ²) and sets an empty vector u_(i). From operation 332, themethod 300 proceeds to operation 334, where the internet classificationsystem 114, for n=0, 1, . . . , N−1, tests each element in z_(i) whetherz_(i) [n]>(m_(i)+γσ_(i)), where γ is a configurable threshold. If theresult of a test is true, the method 300 proceeds to operation 336,where the internet classification system 114 appends the time index n asu_(i)=[u_(i); n]. Else, the method 300 returns to operation 334 for thenext value of n and repeats until N−1.

From operation 336, the method 300 proceeds to operation 338, whereafter the end of N loops with operations 334, 336, the internetclassification system 114 calculates D_(i)[n]=u_(i)[n+1]−u_(i)[n] forn=0, 1, . . . , (L−2), where L is the total length of u_(i).

From operation 338, the method 300 proceeds to operation 340, where theinternet classification system 114 calculates if

${\frac{\max\left( {{hist}\left( d_{i} \right)} \right)}{L - 1} > T_{d}},$where hist(d_(i)) counts the number of occurrences with distinct valuesin d_(i), and T_(d) is a configurable threshold. If true, the method 300proceeds from operation 340 to operation 342, where the internetclassification system 114 classifies the traffic type as consumertraffic and M2M traffic. If false, the method 300 proceeds to operation344, where the internet classification system 114 classifies traffictype as consumer traffic and unknown traffic. After operation 342 or344, the method 300 proceeds to operation 326, shown in FIG. 3A, wherethe internet classification system 114 sets i←i+1 and updates x_(i) tox_(i+1), where x_(i+1)=[x_(i+1), x_(i+2), . . . , x_(i+N)]^(T); and letsδ_(i)=x_(i+N)−x_(i), updates y_(i) to y_(i+1) via recursive DFT suchthat y_(i+1)[n]=w^(−n)(y_(i)[n]+δ_(i)) for n=0, 1, . . . , N−1. Fromoperation 326, the method 300 returns to the loop started at operation306 and the method 300 continues as described.

Turning now to FIG. 4, a diagram illustrating aspects of consumertraffic and M2M traffic before and after applying a DFT in accordancewith the methodologies described herein above will be described,according to an illustrative example. An example of typical consumertraffic (400) is shown as being close to a periodic sinusoid with asingle dominant high spike in frequency spectra, while typical M2Mtraffic (402) has evenly distributed spectrum with multiple identicalspikes. When both consumer traffic and M2M traffic are contained in thenetwork traffic, due to the high spike with consumer traffic, it iseasier to identify and subtract consumer traffic from M2M traffic (seeoperation 330 in FIG. 3A) in the frequency domain rather than in thetime domain—as shown in 404, 406.

Turning now to FIG. 5 is a block diagram illustrating a computer system500 configured to provide the functionality in accordance with variousembodiments of the concepts and technologies disclosed herein. Thesystems, devices, and other components disclosed herein can utilize, atleast in part, an architecture that is the same as or at least similarto the architecture of the computer system 500. For example, the UE(s)102, M2M device(s) 108, and/or the Internet traffic classificationsystem 114 can utilize, at least in part, an architecture that is thesame as or at least similar to the architecture of the computer system500. It should be understood, however, that modification to thearchitecture may be made to facilitate certain interactions amongelements described herein.

The computer system 500 includes a processing unit 502, a memory 504,one or more user interface devices 506, one or more I/O devices 508, andone or more network devices 510, each of which is operatively connectedto a system bus 512. The bus 512 enables bi-directional communicationbetween the processing unit 502, the memory 504, the user interfacedevices 506, the I/O devices 508, and the network devices 510.

The processing unit 502 may be a standard central processor thatperforms arithmetic and logical operations, a more specific purposeprogrammable logic controller (“PLC”), a programmable gate array, orother type of processor known to those skilled in the art and suitablefor controlling the operation of the server computer. Processing unitsare generally known, and therefore are not described in further detailherein.

The memory 504 communicates with the processing unit 502 via the systembus 512. In some embodiments, the memory 504 is operatively connected toa memory controller (not shown) that enables communication with theprocessing unit 502 via the system bus 512. The illustrated memory 504includes an operating system 514 and one or more program modules 516.The operating system 514 can include, but is not limited to, members ofthe WINDOWS, WINDOWS CE, and/or WINDOWS MOBILE families of operatingsystems from MICROSOFT CORPORATION, the LINUX family of operatingsystems, the SYMBIAN family of operating systems from SYMBIAN LIMITED,the BREW family of operating systems from QUALCOMM CORPORATION, the MACOS, OS X, and/or iOS families of operating systems from APPLECORPORATION, the FREEBSD family of operating systems, the SOLARIS familyof operating systems from ORACLE CORPORATION, other operating systems,and the like.

The program modules 516 may include various software and/or programmodules to perform the various operations described herein. The programmodules 516 and/or other programs can be embodied in computer-readablemedia containing instructions that, when executed by the processing unit502, perform various operations such as those described herein.According to embodiments, the program modules 516 may be embodied inhardware, software, firmware, or any combination thereof.

By way of example, and not limitation, computer-readable media mayinclude any available computer storage media or communication media thatcan be accessed by the computer system 500. Communication media includescomputer-readable instructions, data structures, program modules, orother data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any delivery media. The term “modulateddata signal” means a signal that has one or more of its characteristicschanged or set in a manner as to encode information in the signal. Byway of example, and not limitation, communication media includes wiredmedia such as a wired network or direct-wired connection, and wirelessmedia such as acoustic, RF, infrared and other wireless media.Combinations of the any of the above should also be included within thescope of computer-readable media.

Computer storage media includes volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules, or other data. Computer storage media includes, but isnot limited to, RAM, ROM, Erasable Programmable ROM (“EPROM”),Electrically Erasable Programmable ROM (“EEPROM”), flash memory or othersolid state memory technology, CD-ROM, digital versatile disks (“DVD”),or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store the desired information and which can beaccessed by the computer system 500. In the claims, the phrase “computerstorage medium” and variations thereof does not include waves or signalsper se and/or communication media.

The user interface devices 506 may include one or more devices withwhich a user accesses the computer system 500. The user interfacedevices 506 may include, but are not limited to, computers, servers,personal digital assistant (“PDAs”), cellular phones, or any suitablecomputing devices. The I/O devices 508 enable a user to interface withthe program modules 516. In one embodiment, the I/O devices 508 areoperatively connected to an I/O controller (not shown) that enablescommunication with the processing unit 502 via the system bus 512. TheI/O devices 508 may include one or more input devices, such as, but notlimited to, a keyboard, a mouse, or an electronic stylus. Further, theI/O devices 508 may include one or more output devices, such as, but notlimited to, a display screen or a printer. In some embodiments, the I/Odevices 508 can be used for manual controls for operations to exerciseunder certain emergency situations.

The network devices 510 enable the computer system 500 to communicatewith other networks or remote systems via a network 518. Examples of thenetwork devices 510 include, but are not limited to, a modem, a radiofrequency (“RF”) or infrared (“IR”) transceiver, a telephonic interface,a bridge, a router, or a network card. The network 518 may be or mayinclude a wireless network such as, but not limited to, a Wireless LocalArea Network (“WLAN”), a Wireless Wide Area Network (“WWAN”), a WirelessPersonal Area Network (“WPAN”) such as provided via BLUETOOTHtechnology, a Wireless Metropolitan Area Network (“WMAN”) such as aWiMAX network or metropolitan cellular network. Alternatively, thenetwork 518 may be or may include a wired network such as, but notlimited to, a Wide Area Network (“WAN”), a wired Personal Area Network(“PAN”), or a wired Metropolitan Area Network (“MAN”). The network 518can be or can include the network 110 (see FIG. 1), or any other networkor combination of networks described herein.

Turning now to FIG. 6, an illustrative mobile device 600 and componentsthereof will be described. In some embodiments, the UE(s) 102 and/or theM2M device(s) 108 can be configured like the mobile device 600. Whileconnections are not shown between the various components illustrated inFIG. 6, it should be understood that some, none, or all of thecomponents illustrated in FIG. 6 can be configured to interact with oneother to carry out various device functions. In some embodiments, thecomponents are arranged so as to communicate via one or more busses (notshown). Thus, it should be understood that FIG. 6 and the followingdescription are intended to provide a general understanding of asuitable environment in which various aspects of embodiments can beimplemented, and should not be construed as being limiting in any way.

As illustrated in FIG. 6, the mobile device 600 can include a display602 for displaying data. According to various embodiments, the display602 can be configured to display various graphical user interface(“GUI”) elements, text, images, video, virtual keypads and/or keyboards,messaging data, notification messages, metadata, internet content,device status, time, date, calendar data, device preferences, map andlocation data, combinations thereof, and/or the like. The mobile device600 also can include a processor 604 and a memory or other data storagedevice (“memory”) 606. The processor 604 can be configured to processdata and/or can execute computer-executable instructions stored in thememory 606. The computer-executable instructions executed by theprocessor 604 can include, for example, an operating system 608, one ormore applications 610, other computer-executable instructions stored ina memory 606, or the like. In some embodiments, the applications 610also can include a user interface (“UI”) application (not illustrated inFIG. 6).

The UI application can interface with the operating system 608 tofacilitate user interaction with functionality and/or data stored at themobile device 600 and/or stored elsewhere. In some embodiments, theoperating system 608 can include a member of the SYMBIAN OS family ofoperating systems from SYMBIAN LIMITED, a member of the WINDOWS MOBILEOS and/or WINDOWS PHONE OS families of operating systems from MICROSOFTCORPORATION, a member of the PALM WEBOS family of operating systems fromHEWLETT PACKARD CORPORATION, a member of the BLACKBERRY OS family ofoperating systems from RESEARCH IN MOTION LIMITED, a member of the IOSfamily of operating systems from APPLE INC., a member of the ANDROID OSfamily of operating systems from GOOGLE INC., and/or other operatingsystems. These operating systems are merely illustrative of somecontemplated operating systems that may be used in accordance withvarious embodiments of the concepts and technologies described hereinand therefore should not be construed as being limiting in any way.

The UI application can be executed by the processor 604 to aid a userentering content, viewing account information, answering/initiatingcalls, entering/deleting data, entering and setting user IDs andpasswords for device access, configuring settings, manipulating addressbook content and/or settings, multimode interaction, interacting withother applications 610, and otherwise facilitating user interaction withthe operating system 608, the applications 610, and/or other types orinstances of data 612 that can be stored at the mobile device 600.According to various embodiments, the applications 610 can include, forexample, presence applications, visual voice mail applications,messaging applications, text-to-speech and speech-to-text applications,add-ons, plug-ins, email applications, music applications, videoapplications, camera applications, location-based service applications,power conservation applications, game applications, productivityapplications, entertainment applications, enterprise applications,combinations thereof, and the like.

The applications 610, the data 612, and/or portions thereof can bestored in the memory 606 and/or in a firmware 614, and can be executedby the processor 604. The firmware 614 also can store code for executionduring device power up and power down operations. It can be appreciatedthat the firmware 614 can be stored in a volatile or non-volatile datastorage device including, but not limited to, the memory 606 and/or aportion thereof.

The mobile device 600 also can include an input/output (“I/O”) interface616. The I/O interface 616 can be configured to support the input/outputof data such as database data, location information, user information,organization information, presence status information, user IDs,passwords, and application initiation (start-up) requests. In someembodiments, the I/O interface 616 can include a hardwire connectionsuch as universal serial bus (“USB”) port, a mini-USB port, a micro-USBport, an audio jack, a PS2 port, an Institute of Electrical andElectronics Engineers (“IEEE”) 1394 (“FIREWIRE”) port, a serial port, aparallel port, an Ethernet (RJ45) port, an RHO port, a proprietary port,combinations thereof, or the like. In some embodiments, the mobiledevice 600 can be configured to synchronize with another device totransfer content to and/or from the mobile device 600. In someembodiments, the mobile device 600 can be configured to receive updatesto one or more of the applications 610 via the I/O interface 616, thoughthis is not necessarily the case. In some embodiments, the I/O interface616 accepts I/O devices such as keyboards, keypads, mice, interfacetethers, printers, plotters, external storage, touch/multi-touchscreens, touch pads, trackballs, joysticks, microphones, remote controldevices, displays, projectors, medical equipment (e.g., stethoscopes,heart monitors, and other health metric monitors), modems, routers,external power sources, docking stations, combinations thereof, and thelike. It should be appreciated that the I/O interface 616 may be usedfor communications between the mobile device 600 and a network device orlocal device.

The mobile device 600 also can include a communications component 618.The communications component 618 can be configured to interface with theprocessor 604 to facilitate wired and/or wireless communications withone or more networks such as one or more IP access networks and/or oneor more circuit access networks. In some embodiments, other networksinclude networks that utilize non-cellular wireless technologies such asWI-FI or WIMAX. In some embodiments, the communications component 618includes a multimode communications subsystem for facilitatingcommunications via the cellular network and one or more other networks.

The communications component 618, in some embodiments, includes one ormore transceivers. The one or more transceivers, if included, can beconfigured to communicate over the same and/or different wirelesstechnology standards with respect to one another. For example, in someembodiments one or more of the transceivers of the communicationscomponent 618 may be configured to communicate using GSM, CDMA ONE,CDMA2000, LTE, and various other 2G, 2.5G, 3G, 4G, and greatergeneration technology standards. Moreover, the communications component618 may facilitate communications over various channel access methods(which may or may not be used by the aforementioned standards)including, but not limited to, Time-Division Multiple Access (“TDMA”),Frequency-Division Multiple Access (“FDMA”), Wideband CDMA (“W-CDMA”),Orthogonal Frequency-Division Multiplexing (“OFDM”), Space-DivisionMultiple Access (“SDMA”), and the like.

In addition, the communications component 618 may facilitate datacommunications using Generic Packet Radio Service (“GPRS”), EnhancedData Rates for Global Evolution (“EDGE”), the High-Speed Packet Access(“HSPA”) protocol family including High-Speed Download Packet Access(“HSDPA”), Enhanced Uplink (“EUL”) or otherwise termed High-Speed UploadPacket Access (“HSUPA”), HSPA+, and various other current and futurewireless data access standards. In the illustrated embodiment, thecommunications component 618 can include a first transceiver (“TxRx”)620A that can operate in a first communications mode (e.g., GSM). Thecommunications component 618 also can include an N^(th) transceiver(“TxRx”) 620N that can operate in a second communications mode relativeto the first transceiver 620A (e.g., UMTS). While two transceivers620A-620N (hereinafter collectively and/or generically referred to as“transceivers 620”) are shown in FIG. 6, it should be appreciated thatless than two, two, and/or more than two transceivers 620 can beincluded in the communications component 618.

The communications component 618 also can include an alternativetransceiver (“Alt TxRx”) 622 for supporting other types and/or standardsof communications. According to various contemplated embodiments, thealternative transceiver 622 can communicate using various communicationstechnologies such as, for example, WI-FI, WIMAX, BLUETOOTH, infrared,infrared data association (“IRDA”), near-field communications (“NFC”),ZIGBEE, other radio frequency (“RF”) technologies, combinations thereof,and the like.

In some embodiments, the communications component 618 also canfacilitate reception from terrestrial radio networks, digital satelliteradio networks, internet-based radio service networks, combinationsthereof, and the like. The communications component 618 can process datafrom a network such as the Internet, an intranet, a broadband network, aWI-FI hotspot, an Internet service provider (“ISP”), a digitalsubscriber line (“DSL”) provider, a broadband provider, combinationsthereof, or the like.

The mobile device 600 also can include one or more sensors 624. Thesensors 624 can include temperature sensors, light sensors, air qualitysensors, movement sensors, orientation sensors, noise sensors, proximitysensors, or the like. As such, it should be understood that the sensors624 can include, but are not limited to, accelerometers, magnetometers,gyroscopes, infrared sensors, noise sensors, microphones, combinationsthereof, or the like. Additionally, audio capabilities for the mobiledevice 600 may be provided by an audio I/O component 626. The audio I/Ocomponent 626 of the mobile device 600 can include one or more speakersfor the output of audio signals, one or more microphones for thecollection and/or input of audio signals, and/or other audio inputand/or output devices.

The illustrated mobile device 600 also can include a subscriber identitymodule (“SIM”) system 628. The SIM system 628 can include a universalSIM (“USIM”), a universal integrated circuit card (“UICC”) and/or otheridentity devices. The SIM system 628 can include and/or can be connectedto or inserted into an interface such as a slot interface 630. In someembodiments, the slot interface 630 can be configured to acceptinsertion of other identity cards or modules for accessing various typesof networks. Additionally, or alternatively, the slot interface 630 canbe configured to accept multiple subscriber identity cards. Becauseother devices and/or modules for identifying users and/or the mobiledevice 600 are contemplated, it should be understood that theseembodiments are illustrative, and should not be construed as beinglimiting in any way.

The mobile device 600 also can include an image capture and processingsystem 632 (“image system”). The image system 632 can be configured tocapture or otherwise obtain photos, videos, and/or other visualinformation. As such, the image system 632 can include cameras, lenses,charge-coupled devices (“CCDs”), combinations thereof, or the like. Themobile device 600 may also include a video system 634. The video system634 can be configured to capture, process, record, modify, and/or storevideo content. Photos and videos obtained using the image system 632 andthe video system 634, respectively, may be added as message content to amultimedia message service (“MMS”) message, email message, and sent toanother mobile device. The video and/or photo content also can be sharedwith other devices via various types of data transfers via wired and/orwireless communication devices as described herein.

The mobile device 600 also can include one or more location components636. The location components 636 can be configured to send and/orreceive signals to determine a geographic location of the mobile device600. According to various embodiments, the location components 636 cansend and/or receive signals from global positioning system (“GPS”)devices, assisted GPS (“A-GPS”) devices, WI-FI/WIMAX and/or cellularnetwork triangulation data, combinations thereof, and the like. Thelocation component 636 also can be configured to communicate with thecommunications component 618 to retrieve triangulation data fordetermining a location of the mobile device 600. In some embodiments,the location component 636 can interface with cellular network nodes,telephone lines, satellites, location transmitters and/or beacons,wireless network transmitters and receivers, combinations thereof, andthe like. In some embodiments, the location component 636 can includeand/or can communicate with one or more of the sensors 624 such as acompass, an accelerometer, and/or a gyroscope to determine theorientation of the mobile device 600. Using the location component 636,the mobile device 600 can generate and/or receive data to identify itsgeographic location, or to transmit data used by other devices todetermine the location of the mobile device 600. The location component636 may include multiple components for determining the location and/ororientation of the mobile device 600.

The illustrated mobile device 600 also can include a power source 638.The power source 638 can include one or more batteries, power supplies,power cells, and/or other power subsystems including alternating current(“AC”) and/or direct current (“DC”) power devices. The power source 638also can interface with an external power system or charging equipmentvia a power I/O component 640. Because the mobile device 600 can includeadditional and/or alternative components, the above embodiment should beunderstood as being illustrative of one possible operating environmentfor various embodiments of the concepts and technologies describedherein. The described embodiment of the mobile device 600 isillustrative, and should not be construed as being limiting in any way.

Turning now to FIG. 7, details of a network 700 are illustrated,according to an illustrative embodiment. The network 700 includes acellular network 702, a packet data network 704, and a circuit switchednetwork 706, for example, a publicly switched telephone network(“PSTN”). In some embodiments, the network 110 introduced above in FIG.1 can be configure the same as or like the network 700.

The cellular network 702 includes various components such as, but notlimited to, base transceiver stations (“BTSs”), nodeBs (“NBs”), eNBs,base station controllers (“BSCs”), radio network controllers (“RNCs”),mobile switching centers (“MSCs”), MMES, SGWs, PGWs, short messageservice centers (“SMSCs”), multimedia messaging service centers(“MMSCs”), home location registers (“HLRs”), home subscriber servers(“HS Ss”), visitor location registers (“VLRs”), charging platforms,billing platforms, voicemail platforms, GPRS core network components,location service nodes, an IP Multimedia Subsystem (“IMS”), and thelike. The cellular network 702 also includes radios and nodes forreceiving and transmitting voice, data, and combinations thereof to andfrom radio transceivers, networks, the packet data network 704, and thecircuit switched network 706.

A mobile communications device 708, such as, for example, the UE 102,the M2M device 108, a computing device, a cellular telephone, a mobileterminal, a PDA, a laptop computer, a handheld computer, andcombinations thereof, can be operatively connected to the cellularnetwork 702. The cellular network 702 can be configured as a 2G GSMnetwork and can provide data communications via GMPRS and/or EDGE.Additionally, or alternatively, the cellular network 702 can beconfigured as a 3G UMTS network and can provide data communications viathe HSPA protocol family, for example, HSDPA, EUL (also referred to asHSUPA), and HSPA+. The cellular network 702 also is compatible with 4Gmobile communications standards as well as evolved and future mobilestandards.

The packet data network 704 includes various devices, for example,servers, computers, databases, and other devices in communication withone another, as is generally known. The packet data network 704 devicesare accessible via one or more network links. The servers often storevarious files that are provided to a requesting device such as, forexample, a computer, a terminal, a smartphone, or the like. Typically,the requesting device includes software (a “browser”) for executing aweb page in a format readable by the browser or other software. Otherfiles and/or data may be accessible via “links” in the retrieved files,as is generally known. In some embodiments, the packet data network 704includes or is in communication with the Internet. The circuit switchednetwork 706 includes various hardware and software for providing circuitswitched communications. The circuit switched network 706 may include,or may be, what is often referred to as a plain old telephone system(“POTS”). The functionality of a circuit switched network 706 or othercircuit-switched network are generally known and will not be describedherein in detail.

The illustrated cellular network 702 is shown in communication with thepacket data network 704 and a circuit switched network 706, though itshould be appreciated that this is not necessarily the case. One or moreInternet-capable devices 710, for example, a PC, a laptop, a portabledevice, or another suitable device, can communicate with one or morecellular networks 702, and devices connected thereto, through the packetdata network 704. It also should be appreciated that theInternet-capable device 710 can communicate with the packet data network704 through the circuit switched network 706, the cellular network 702,and/or via other networks (not illustrated).

As illustrated, a communications device 712, for example, a telephone,facsimile machine, modem, computer, or the like, can be in communicationwith the circuit switched network 706, and therethrough to the packetdata network 704 and/or the cellular network 702. It should beappreciated that the communications device 712 can be anInternet-capable device, and can be substantially similar to theInternet-capable device 710. In the specification, the network 700 isused to refer broadly to any combination of the networks 702, 704, 706.It should be appreciated that substantially all of the functionalitydescribed with reference to the network 700 can be performed by thecellular network 702, the packet data network 704, and/or the circuitswitched network 706, alone or in combination with other networks,network elements, and the like.

Turning now to FIG. 8, a network functions virtualization platform(“NFVP”) 800 will be described, according to an exemplary embodiment.The architecture of the NFVP 800 can be used to implement, at least inpart, the internet traffic classification system 114 in someembodiments. The NFVP 800 is a shared infrastructure that can supportmultiple services and network applications. The illustrated NFVP 800includes a hardware resource layer 802, a virtualization/control layer804, and a virtual resource layer 806 that work together to performoperations as will be described in detail herein.

The hardware resource layer 802 provides hardware resources, which, inthe illustrated embodiment, include one or more compute resources 808,one or more memory resources 810, and one or more other resources 812.The compute resource(s) 808 can include one or more hardware componentsthat perform computations to process data, and/or to executecomputer-executable instructions of one or more application programs,operating systems, and/or other software. The compute resources 808 caninclude one or more central processing units (“CPUs”) configured withone or more processing cores. The compute resources 808 can include oneor more graphics processing unit (“GPU”) configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the compute resources 808 can includeone or more discrete GPUs. In some other embodiments, the computeresources 808 can include CPU and GPU components that are configured inaccordance with a co-processing CPU/GPU computing model, wherein thesequential part of an application executes on the CPU and thecomputationally-intensive part is accelerated by the GPU. The computeresources 808 can include one or more system-on-chip (“SoC”) componentsalong with one or more other components, including, for example, one ormore of the memory resources 810, and/or one or more of the otherresources 812. In some embodiments, the compute resources 808 can be orcan include one or more SNAPDRAGON SoCs, available from QUALCOMM of SanDiego, Calif.; one or more TEGRA SoCs, available from NVIDIA of SantaClara, Calif.; one or more HUMMINGBIRD SoCs, available from SAMSUNG ofSeoul, South Korea; one or more Open Multimedia Application Platform(“OMAP”) SoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one ormore customized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The compute resources 808 can be or can include one ormore hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the compute resources 808 can be or caninclude one or more hardware components architected in accordance withan x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the compute resources 808 canutilize various computation architectures, and as such, the computeresources 808 should not be construed as being limited to any particularcomputation architecture or combination of computation architectures,including those explicitly disclosed herein.

The memory resource(s) 810 can include one or more hardware componentsthat perform storage operations, including temporary or permanentstorage operations. In some embodiments, the memory resource(s) 810include volatile and/or non-volatile memory implemented in any method ortechnology for storage of information such as computer-readableinstructions, data structures, program modules, or other data disclosedherein. Computer storage media includes, but is not limited to, randomaccess memory (“RAM”), read-only memory (“ROM”), Erasable ProgrammableROM (“EPROM”), Electrically Erasable Programmable ROM (“EEPROM”), flashmemory or other solid state memory technology, CD-ROM, digital versatiledisks (“DVD”), or other optical storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store data and which can be accessedby the compute resources 808.

The other resource(s) 812 can include any other hardware resources thatcan be utilized by the compute resources(s) 808 and/or the memoryresource(s) 810 to perform operations described herein. The otherresource(s) 812 can include one or more input and/or output processors(e.g., network interface controller or wireless radio), one or moremodems, one or more codec chipset, one or more pipeline processors, oneor more fast Fourier transform (“FFT”) processors, one or more digitalsignal processors (“DSPs”), one or more speech synthesizers, and/or thelike.

The hardware resources operating within the hardware resource layer 802can be virtualized by one or more virtual machine monitors (“VMMs”)814A-814K (also known as “hypervisors”; hereinafter “VMMs 814”)operating within the virtualization/control layer 804 to manage one ormore virtual resources that reside in the virtual resource layer 806.The VMMs 814 can be or can include software, firmware, and/or hardwarethat alone or in combination with other software, firmware, and/orhardware, manages one or more virtual resources operating within thevirtual resource layer 806.

The virtual resources operating within the virtual resource layer 806can include abstractions of at least a portion of the compute resources808, the memory resources 810, the other resources 812, or anycombination thereof. These abstractions are referred to herein asvirtual machines (“VMs”). In the illustrated embodiment, the virtualresource layer 806 includes VMs 816A-816N (hereinafter “VMs 816”). Eachof the VMs 816 can execute one or more applications.

Based on the foregoing, it should be appreciated that concepts andtechnologies directed to internet traffic classification viatime-frequency analysis have been disclosed herein. Although the subjectmatter presented herein has been described in language specific tocomputer structural features, methodological and transformative acts,specific computing machinery, and computer-readable media, it is to beunderstood that the concepts and technologies disclosed herein are notnecessarily limited to the specific features, acts, or media describedherein. Rather, the specific features, acts and mediums are disclosed asexample forms of implementing the concepts and technologies disclosedherein.

The subject matter described above is provided by way of illustrationonly and should not be construed as limiting. Various modifications andchanges may be made to the subject matter described herein withoutfollowing the example embodiments and applications illustrated anddescribed, and without departing from the true spirit and scope of theembodiments of the concepts and technologies disclosed herein.

We claim:
 1. An Internet traffic classification system comprising: aprocessor; and a memory comprising instructions that, when executed bythe processor, cause the processor to perform operations comprisingreceiving an internet traffic sequence comprising non-malicious datapackets and malicious data packets, extracting, from the internettraffic sequence, a plurality of consecutive samples to be used forclassification of the internet traffic sequence, converting theplurality of consecutive samples of the internet traffic sequence from atime domain to a frequency domain via a recursive discrete Fouriertransform, determining whether a largest power spectrum in the pluralityof consecutive samples of the internet traffic sequence is greater thana threshold portion of a total power spectra of the plurality ofconsecutive samples of the internet traffic sequence, when the largestpower spectrum in the plurality of consecutive samples of the internettraffic sequence is greater than the threshold portion of the totalpower spectra, determining that the plurality of consecutive samples ofthe internet traffic sequence comprises a consumer traffic component,and removing, from the plurality of consecutive samples of the internettraffic sequence, any samples of the plurality of consecutive samplescorresponding to the consumer traffic component, calculating a mean anda variance of a remaining portion of the internet traffic sequence,wherein the remaining portion of the internet traffic sequencecomprising the plurality of consecutive samples without any samplescorresponding to the consumer traffic component, setting, based upon themean and the variance of the remaining portion of the internet trafficsequence, a threshold for detection of machine-to-machine traffic,recording a series of time indices for samples in the remaining portionof the internet traffic sequence that are greater than the threshold fordetection of machine-to-machine traffic, computing time differencesbetween adjacent time indices within the series of time indices,creating a histogram using the time differences, counting the histogram,and when most occurrences in the histogram are in association with aspecific time difference, determining that the remaining portion of theinternet traffic sequence comprises a machine-to-machine-trafficcomponent.
 2. The internet traffic classification system of claim 1,wherein the operations further comprise classifying the internet trafficsequence as comprising the consumer traffic component only.
 3. Theinternet traffic classification system of claim 1, wherein theoperations further comprise classifying the internet traffic sequence ascomprising the machine-to-machine traffic component only.
 4. Theinternet traffic classification system of claim 1, wherein theoperations further comprise classifying the internet traffic sequence ascomprising the consumer traffic component and the machine-to-machinetraffic component.
 5. The internet traffic classification system ofclaim 1, wherein the operations further comprise classifying theinternet traffic sequence as comprising an unknown traffic component. 6.The internet traffic classification system of claim 1, wherein theoperations are performed through a sliding window that focuses on onesample of the plurality of consecutive samples.
 7. The Internet trafficclassification system of claim 1, wherein the Internet traffic sequencecomprises real-time Internet traffic.
 8. A computer-readable storagemedium comprising computer-executable instructions that, when executedby a processor, cause the processor to perform operations comprising:receiving an Internet traffic sequence comprising non-malicious datapackets and malicious data packets, extracting, from the Internettraffic sequence, a plurality of consecutive samples to be used forclassification of the Internet traffic sequence, converting theplurality of consecutive samples of the Internet traffic sequence from atime domain to a frequency domain via a recursive discrete Fouriertransform, determining whether a largest power spectrum in the pluralityof consecutive samples of the internet traffic sequence is greater thana threshold portion of a total power spectra of the plurality ofconsecutive samples of the Internet traffic sequence, when the largestpower spectrum in the plurality of consecutive samples of the Internettraffic sequence is greater than the threshold portion of the totalpower spectra, determining that the plurality of consecutive samples ofthe Internet traffic sequence comprises a consumer traffic component,and removing, from the plurality of consecutive samples of the Internettraffic sequence, any samples of the plurality of consecutive samplescorresponding to the consumer traffic component, calculating a mean anda variance of a remaining portion of the Internet traffic sequence,wherein the remaining portion of the Internet traffic sequencecomprising the plurality of consecutive samples without any samplescorresponding to the consumer traffic component, setting, based upon themean and the variance of the remaining portion of the Internet trafficsequence, a threshold for detection of machine-to-machine traffic,recording a series of time indices for samples in the remaining portionof the Internet traffic sequence that are greater than the threshold fordetection of machine-to-machine traffic, computing time differencesbetween adjacent time indices within the series of time indices,creating a histogram using the time differences, counting the histogram,and when most occurrences in the histogram are in association with aspecific time difference, determining that the remaining portion of theinternet traffic sequence comprises a machine-to-machine-trafficcomponent.
 9. The computer-readable storage medium of claim 8, whereinthe operations further comprise classifying the internet trafficsequence as comprising the consumer traffic component only.
 10. Thecomputer-readable storage medium of claim 8, wherein the operationsfurther comprise classifying the internet traffic sequence as comprisingthe machine-to-machine traffic component only.
 11. The computer-readablestorage medium of claim 8, wherein the operations further compriseclassifying the internet traffic sequence as comprising the consumertraffic component and the machine-to-machine traffic component.
 12. Thecomputer-readable storage medium of claim 8, wherein the operationsfurther comprise classifying the internet traffic sequence as comprisingan unknown traffic component.
 13. The computer-readable storage mediumof claim 8, wherein the operations are performed through a slidingwindow that focuses on one sample of the plurality of consecutivesamples.
 14. A method comprising: receiving, by an internet trafficclassification system comprising a processor, an internet trafficsequence comprising non-malicious data packets and malicious datapackets; extracting, by the internet traffic classification system, fromthe internet traffic sequence, a plurality of consecutive samples to beused for classification of the internet traffic sequence; converting, bythe internet traffic classification system, the plurality of consecutivesamples of the internet traffic sequence from a time domain to afrequency domain via a recursive discrete Fourier transform;determining, by the internet traffic classification system, whether alargest power spectrum in the plurality of consecutive samples of theinternet traffic sequence is greater than a threshold portion of a totalpower spectra of the plurality of consecutive samples of the internettraffic sequence; when the largest power spectrum in the plurality ofconsecutive samples of the internet traffic sequence is greater than thethreshold portion of the total power spectra, determining, by theinternet traffic classification system, that the plurality ofconsecutive samples of the internet traffic sequence comprises aconsumer traffic component, and removing, by the internet trafficclassification system, from the plurality of consecutive samples of theinternet traffic sequence, any samples of the plurality of consecutivesamples corresponding to the consumer traffic component; calculating, bythe internet traffic classification system, a mean and a variance of aremaining portion of the internet traffic sequence, wherein theremaining portion of the internet traffic sequence comprising theplurality of consecutive samples without any samples corresponding tothe consumer traffic component; setting, by the internet trafficclassification system, based upon the mean and the variance of theremaining portion of the internet traffic sequence, a threshold fordetection of machine-to-machine traffic; recording, by the internettraffic classification system, a series of time indices for samples inthe remaining portion of the internet traffic sequence that are greaterthan the threshold for detection of machine-to-machine traffic;computing, by the internet traffic classification system, timedifferences between adjacent time indices within the series of timeindices; creating, by the internet traffic classification system, ahistogram using the time differences; counting, by the internet trafficclassification system, the histogram; and when most occurrences in thehistogram are in association with a specific time difference,determining, by the internet traffic classification system, that theremaining portion of the internet traffic sequence comprises amachine-to-machine-traffic component.
 15. The method of claim 14,further comprising classifying the internet traffic sequence ascomprising the consumer traffic component only.
 16. The method of claim14, further comprising classifying the internet traffic sequence ascomprising the machine-to-machine component only.
 17. The method ofclaim 14, further comprising classifying the internet traffic sequenceas comprising the consumer traffic component and the machine-to-machinetraffic component.
 18. The method of claim 14, further comprisingclassifying the internet traffic sequence as comprising an unknowntraffic component.